System and Method for Managing Mixed Fleet Worksites Using Video and Audio Analytics

ABSTRACT

Systems and methods for managing and optimizing mixed fleet worksite operations based on video and or audio data are disclosed. One method includes receiving one or more models relating to a fleet of machines at the worksite, wherein the fleet of machines comprises an in-network machine and an out-of-network machine, receiving first sensor data associated with the out-of-network machine at the worksite, receiving second sensor data associated with the in-network machine at the worksite, determining a machine state of each of the in-network machine and the out-of-network machine based at least on the first sensor data and the second sensor data, comparing the determined machine states to a modeled machine state represented by the received one or more models to classify site operations and/or detect an irregularity in site operations or an inefficiency in site operations, and generating a response based at least on the detected irregularity or inefficiency.

TECHNICAL FIELD

This disclosure relates generally to worksite operations involving heavymachinery, and more particularly to a system and method for managing amixed fleet worksite using video and/or audio analytics.

BACKGROUND

A worksite, such as a mining or construction site, will typicallyinclude a variety of machines, such as bulldozers, excavators, dumptrucks, and the like, working cooperatively to accomplish a particulartask. In order to accomplish the task efficiently, the operation,availability, and mechanical status of the machines may be tracked andcoordinated to ensure that each machine is used to its maximum benefit.For example, if a worksite included an excavator filling dump truckswith material, a shortfall of dump trucks would result in the excavatorsitting idly while waiting for an empty dump truck to receive theexcavated material. An important factor in coordinating the machines foroptimal efficiency is the machine state of various machines operating atthe worksite. However, certain worksites may be a fixed fleet worksiteincluding various models of machines from the same or differentmanufactures. As such, some machines may not have the ability tocommunicate state information with other machines at the same worksite.

One method for analyzing a change in machine operations is disclosed inU.S. Pat. Appl. Pub. No. 2014/0247347 to McNeill et al. (the '347application). The '347 application describes a camera or video systemfor monitoring, analyzing and controlling the operation of a machine(e.g., a corrugated-paper-processing machine). In some examples, thecamera system includes one or more video cameras and video analytics foridentifying one or more states and/or changes in state for a process orflow, such as distinguishing between a first state of the machine, suchas a steady-state flow and a second state or states of the machine, suchas a jam state or states, and/or a state or states of impending jam ofthe machine or articles operated on by the machine.

Although the '347 application describes a method that may help detect achange in simple machine states (e.g., steady-state and jam state ofconveyer, safe zone and pedestrian zone for forktrucks), the method maybe unsuitable for applications involving large machines in an open,expansive worksite such as a construction site or mine site, havingmultiple machine states and external variables complicating the analysisof images. Furthermore, the '347 application does not address thecomplications of having a mixed fleet worksite. These and othershortcomings of the prior art are addressed by this disclosure.

SUMMARY

This disclosure relates to systems and methods for managing andoptimizing mixed fleet worksite operations based on video and or audiodata are disclosed. One method includes receiving, by one or moreprocessors, one or more models relating to a worksite or a fleet ofmachines at the worksite, wherein the fleet of machines comprises anin-network machine and an out-of-network machine, receiving, by the oneor more processors, first sensor data associated with the out-of-networkmachine at the worksite, the first sensor data comprising one or more ofimage data and audio data, receiving, by the one or more processors,second sensor data associated with the in-network machine at theworksite, determining, by the one or more processors, a machine state ofeach of the in-network machine and the out-of-network machine based atleast on the first sensor data and the second sensor data, comparing thedetermined machine states to a modeled machine state represented by thereceived one or more models to detect an irregularity in site operationsor an inefficiency in site operations, and generating a response basedat least on the detected irregularity or inefficiency.

In an aspect, a system may include a processor and a memory bearinginstructions that, upon execution by the processor, cause the processorto: receive one or more models relating to a worksite or a fleet ofmachines at the worksite, wherein the fleet of machines comprises anin-network machine and an out-of-network machine; receive first sensordata associated with the out-of-network machine at the worksite, thefirst sensor data comprising one or more of image data and audio data;receive second sensor data associated with the in-network machine at theworksite; determine a machine state of each of the in-network machineand the out-of-network machine based at least on the first sensor dataand the second sensor data; and compare the determined machine states toa modeled machine state represented by the received one or more modelsto classify one or more site operations.

In an aspect, a computer readable storage medium may bear instructionsthat, upon execution by one or more processors, effectuate operationscomprising receiving, by the one or more processors, one or more modelsrelating to a worksite or a fleet of machines at the worksite, whereinthe fleet of machines comprises an in-network machine and anout-of-network machine, receiving, by the one or more processors, firstsensor data associated with the out-of-network machine at the worksite,the first sensor data comprising one or more of image data and audiodata, receiving, by the one or more processors, second sensor dataassociated with the in-network machine at the worksite, determining, bythe one or more processors, a machine state of each of the in-networkmachine and the out-of-network machine based at least on the firstsensor data and the second sensor data, comparing the determined machinestates to a modeled machine state represented by the received one ormore models to detect an irregularity in site operations or aninefficiency in site operations, and generating a response based atleast on the detected irregularity or inefficiency.

BRIEF DESCRIPTION OF THE DRAWINGS

The following detailed description is better understood when read inconjunction with the appended drawings. For the purposes ofillustration, examples are shown in the drawings; however, the subjectmatter is not limited to the specific elements and instrumentalitiesdisclosed. In the drawings:

FIG. 1 illustrates an exemplary worksite in accordance with aspects ofthe disclosure.

FIG. 2 illustrates a schematic side view of an exemplary machine inaccordance with aspects of the disclosure.

FIG. 3 illustrates a schematic top view of an exemplary machine inaccordance with aspects of the disclosure.

FIG. 4 illustrates a block diagram of an exemplary data flow inaccordance with aspects of the disclosure.

FIG. 5 illustrates a block diagram of an exemplary system and data flowin accordance with aspects of the disclosure.

FIG. 6 illustrates an exemplary method in accordance with aspects of thedisclosure.

FIG. 7 illustrates a block diagram of a computer system configured toimplement the method of FIG. 6.

DETAILED DESCRIPTION

System, methods, and computer readable medium for determining machinestates at a mixed fleet worksite (e.g., construction site, minesite,etc.) using at least video and/or audio analytics are disclosed. In anaspect, visual/audio data may be collected including: satellite-captureddata, drone-captured data, site level video/audio, regional video/audio,machine level video/audio, payload video/audio, machine-to-machinevideo/audio, operator video/audio, and the like. Various filters such asthermal, infrared, and night vision may be applied to the collecteddata. The collected data may be processed (e.g., via a machine learningalgorithm) to determine signatures/fingerprints of various machinestates. The collected data may be compared/overlaid with telematicsdata. Once the signatures are recognized, they may be used to identifymatching signatures from subsequently captured data regardless of themanufacturer of the machine. As an example, machine states may include,full load, empty load, payload material type, payload vs. air ratio(e.g., payload density), payload placement, payload compaction, payloadwater content, material amount moved, drop placement, excavatorposition, idle state, swing, dump position, deformation of machine,operator characteristic, ground crew location relative to machine, andthe like.

A database of machine states may be populated via processing of captureddata. Once a machine state is identified (e.g., independent of fleetmanufacturer), information relating to the machine states may begenerated. For example, reports may be generated relating to productionversus target, safety protocols, best practices, exposure of blastingprocess (e.g., target material density), maintenance schedules,efficiencies in machine operation, and maximizing utilization time, forexample. Furthermore, manufacturer-independent machine stateidentification may be used to manage mixed-fleet mine sites.

FIG. 1 shows a worksite 10 such as, for example, an open pit miningoperation or a construction site. As part of the mining function,various machines may operate at or between different locations of theworksite 10. These machines may include, one or more digging machines12, one or more loading machines 14, one or more hauling machines 16,one or more transport machines (not shown), and/or other types ofmachines known in the art.

In certain aspects, one or more of the machines 12, 14, 16 may be anin-network machine and may be in communication with each other and witha central station 18 by way of wireless communication (such as acommunication channel as defined herein) to remotely transmit andreceive operational data and instructions. In other aspects, one or moreof the machines 12, 14, 16 may be an out-of-network machine and may notbe configured to communicate with each other or with a central station18. As an example, the out-of network machine may be a model of machinethat is not equipped with the communication technology necessary toeffect communication with other machines or the central station 18. Asanother example, the out-of-network machine may be a machine that isprovided by a manufacturer different from the rest of the machinesincluded in the fleet operating at the worksite 10.

Information relating to the machines 12, 14, 16 may be captured via anonsite sensor 19 a such as a video camera, infrared sensor, thermalsensor, audio recorder, RADAR sensor, LIDAR sensor, optical sensor, orthe like. Information relating to the machines 12, 14, 16 may becaptured via an offsite sensor 19 b, which may be similar to the offsitesensor and disposed on a satellite, drone, aircraft, or other offsitedevice. The information captured via the sensors 19 a, 19 b may betransmitted to a processor such as the central station 18 by way ofwireless communication (such as the communication channels definedherein).

The digging machine 12 may refer to any machine that reduces material atthe worksite 10 for the purpose of subsequent operations (e.g., forblasting, loading, and hauling operations). Examples of the diggingmachines 12 may include excavators, backhoes, dozers, drilling machines,trenchers, drag lines, etc. Multiple digging machines 12 may beco-located within a common area at worksite 10 and may perform similarfunctions. As such, under normal conditions, similar co-located diggingmachines 12 should perform about the same with respect to productivityand efficiency when exposed to similar site conditions.

The loading machine 14 may refer to any machine that lifts, carries,and/or loads material that has been reduced by the digging machine 12onto waiting hauling machines 16. Examples of the loading machine 14 mayinclude a wheeled or tracked loader, a front shovel, an excavator, acable shovel, a stack reclaimer, or any other similar machine. One ormore loading machines 14 may operate within common areas of the worksite10 to load reduced materials onto the hauling machines 16. Under normalconditions, similar co-located loading machines 14 should perform aboutthe same with respect to productivity and efficiency when exposed tosimilar site conditions.

The hauling machine 16 may refer to any machine that carries theexcavated materials between different locations within the worksite 10.Examples of the hauling machine 16 may include an articulated truck, anoff-highway truck, an on-highway dump truck, a wheel tractor scraper, orany other similar machine. Laden hauling machines 16 may carryoverburden from areas of excavation within the worksite 10, along haulroads to various dump sites, and return to the same or differentexcavation areas to be loaded again. Under normal conditions, similarco-located hauling machines 16 should perform about the same withrespect to productivity and efficiency when exposed to similar siteconditions. Other machines may be at the worksite and may implementaspects of this disclosure, including compactors, graders, pavers, etc.

FIG. 2 shows one exemplary machine that may be operated at the worksite10. It should be noted that, although the depicted machine may embodythe hauling machine 16, the following description may be equally appliedto any machine operating at the worksite 10. In certain aspects, one ormore of the machines 12, 14, 16 may be an in-network machine and may bein communication with each other and with a central station 18 by way ofwireless communication (such as a communication channel as definedherein) to remotely transmit and receive operational data andinstructions. In certain aspects, one or more of the machines 12, 14, 16may be an out-of-network machine and may not be configured tocommunicate with each other or with a central station 18. In otheraspects, an out-of-network machine may be equipped to communicate withcertain machines 12, 14, 16 and not others in a mixed fleet environment.For example, various communication networks may be configured fordifferent manufacturers or providers and may not be compatible tocross-talk with other communication networks. Accordingly, even themachines 12, 14, 16 that are equipped with wireless communicationtechnology may be out-of-network machines in a particular worksite.

As an example, the hauling machine 16 may record and transmit data tothe central station 18 (referring to FIG. 1) during its operation on acommunication channel as defined herein. Similarly, the central station18 may analyze the data and transmit information to the hauling machine16 on a communication channel as defined herein. The data transmitted tothe central station 18 may include payload data, operator data, machineidentification data, performance data, worksite data, diagnostic data,and other data, which may be automatically monitored from onboard thehauling machine 16 and/or manually observed and input by machineoperators. The information remotely transmitted back to the haulingmachines 16 may include electronic terrain maps, machine configurationcommands, instructions, recommendations and/or the like.

Payload data may include information relating to the material beinghauled, processed, or displaced and may include binary state informationof a material load such as full load or empty load, as well as, dynamicinformation such as payload material type (e.g., aggregate, ore, etc.),payload vs. air ratio (e.g., payload density), payload placement, amountof material moved, compaction, water content, drop placement, and thelike. Payload data may relate to material being moved, compacted,shoveled, hauled, or other type of processing at a worksite. The termmaterial load may refer to any material being processed at the worksiteand is not so limited to a hauling process, for example.

Identification data may include machine-specific data, operator-specificdata, location-specific data and/or the like. Machine-specific data mayinclude identification data associated with a type of machine (e.g.,digging, loading, hauling, etc.), a make and model of machine (e.g.,Caterpillar 797 OHT), a machine manufacture date or age, a usage ormaintenance/repair history, etc. Operator-specific data may include anidentification of a current operator, information about the currentoperator (e.g., a skill or experience level, an authorization level, anamount of time logged during a current shift, a usage history, etc.), ahistory of past operators, and the like. Site-specific data may includea task currently being performed by the operator, a current location atthe worksite 10, a location history, a material composition at aparticular area of the worksite 10, a site-imposed speed limit, etc.

Performance data may include current and historic data associated withoperation of any machine at the worksite 10. Performance data mayinclude, for example, payload information, efficiency information,productivity information, fuel economy information, speed information,traffic information, weather information, road and/or surface conditioninformation, maneuvering information (e.g., braking, steering, wheelslip, etc.), downtime and repair or maintenance information, etc.

Diagnostic data may include recorded parameter information associatedwith specific components and/or systems of the machine. For example,diagnostic data may include engine temperatures, engine pressures,engine and/or ground speeds and acceleration, fluid characteristics(e.g., levels, contamination, viscosity, temperature, pressure, etc.),fuel consumption, engine emissions, braking conditions, transmissioncharacteristics (e.g., shifting, torques, and speed), air and/or exhaustpressures and temperatures, engine calibrations (e.g., injection and/orignition timings), wheel torque, rolling resistance, system voltage,etc. Some diagnostic data may be monitored directly, while other datamay be derived or calculated from the monitored parameters. Diagnosticdata may be used to determine performance data, if desired.

To facilitate the collection, recording, and transmitting of data fromthe machines at the worksite 10 to the central station 18 (referring toFIG. 1) and vice versa, each of the hauling machines 16 may include anonboard control module 20, an operator interface module 22, and acommunication module 24. The communication module 24 may communicateover a communication channel as defined herein. Data received by thecontrol module 20 and/or the operator interface module 22 may be sentoffboard to the central station 18 by way of the communication module24. The communication module 24 may also be used to send instructionsand/or recommendations from the central station 18 to an operator of thehauling machine 16 by way of the operator interface module 22. It iscontemplated that additional or different modules may be includedonboard the hauling machine 16, if desired.

The control module 20 may include a plurality of sensors 20 a, 20 b, 20c distributed throughout the hauling machine 16 and/or the operator andconfigured to gather data from the operator and/or various componentsand subsystems of the hauling machine 16. It is contemplated that agreater or lesser number of sensors may be included than that shown inFIG. 2.

In an aspect, the sensors 20 a-c may include any device that senses,detects, or measures a condition or state of the hauling machine 16. Thesensors 20 a-c may be directed toward sensing a machine state relatingto the operation of the hauling machine 16 relative to a mine model orsimulated plan. As an example, machine states may include, full load,empty load, payload material type, payload vs. air ratio (e.g., payloaddensity), payload placement, drop placement, excavator position, idlestate, swing, dump position, deformation of machine, and the like. Thesensors 20 a-c may include RADAR, LIDAR, infrared, thermal, audio,and/or an image capture device such as a video camera. Machine statesmay relate to any information pertaining to the operation of a machine.Examples of data gathered from the sensors 20 a-c include operatormanipulation of the input devices, tool, or power source, machinevelocity, machine location, fluid pressure, fluid flow rate, fluidtemperature, fluid contamination level, fluid viscosity, electriccurrent level, electric voltage level, fluid (e.g., fuel, water, oil)consumption rates, payload level, payload value, percent of maximumallowable payload limit, payload history, payload distribution,transmission output ratio, cycle time, idle time, grade, recentlyperformed maintenance, or recently performed repair.

In another aspect, the sensors 20 a-c may be associated with a powersource (not shown), a transmission (not shown), a traction device, awork implement, an operator station, and/or other components andsubsystems of the hauling machine 16. These sensors may be configured toprovide data gathered from each of the associated components andsubsystems. Other pieces of information may be generated or maintainedby data control module 20 such as, for example, time of day, date,weather, road or surface conditions, and machine location (global and/orlocal).

The control module 20 may also be in direct communication with theseparate components and subsystems of the hauling machine 16 tofacilitate manual, autonomous, and/or remote monitoring and/or controlof the hauling machine 16. For example, control module 20 may be incommunication with the power source of the hauling machine 16 to controlfueling, the transmission to control shifting, a steering mechanism tocontrol heading, a differential lock to control traction, a brakingmechanism to control deceleration, a tool actuator to control materialdumping, and with other components and/or subsystems of the haulingmachine 16. Based on direct commands from a human operator, remotecommands from the central station 18 or another one of the machines 12,14, 16 at the worksite 10, and/or self-direction, the control module 20may selectively adjust operation of the components and subsystems of thehauling machine 16 to accomplish a predetermined task.

The operator interface module 22 may be located onboard the haulingmachine 16 for collection and/or recording of data. The operatorinterface module 22 may include or be communicatively connected to oneor more operator data input devices such as a press-able button, amovable dial, a keyboard, a touchscreen, a touchpad, a pointing device,or any other means by which an operator may indicate an aspect of his orher condition. For example, the operator interface module 22 may includea touchpad, which may be used by the operator to move a cursor on adisplay screen, such as an LCD screen, to select an indicator of theoperator's condition. The data received via the operator interfacemodule 22 may include observed information associated with the worksite10, the hauling machine 16, and/or the operator.

The communication module 24 may include any device that facilitatescommunication of data between the hauling machine 16 and the centralstation 18, and/or between the machines 12, 14, 16. The communicationmodule 24 may include hardware and/or software that enables sendingand/or receiving data through a wireless communication link 24 a. It iscontemplated that, in some situations, the data may be transferred tothe central station 18 and/or other machines 12, 14, 16 through a directdata link (not shown), or downloaded from the hauling machine 16 anduploaded to the central station 18, if desired. It is also contemplatedthat, in some situations, the data automatically monitored by thecontrol module 20 may be electronically transmitted, while theoperator-observed data may be communicated to the central station 18 bya voice communication device, such as a two-way radio (not shown).

The communication module 24 may also have the ability to record themonitored and/or manually input data. For example, the communicationmodule 24 may include a data recorder (not shown) having a recordingmedium (not shown). In some cases, the recording medium may be portable,and data may be transferred from the hauling machine 16 to the centralstation 18 or between the machines 12, 14, 16 using the portablerecording medium.

FIG. 3 illustrates an exemplary machine 110 having multiple systems andcomponents that cooperate to accomplish a task. The machine 110 mayembody a fixed or mobile machine that performs some type of operationassociated with an industry such as mining, construction, farming,transportation, or any other industry known in the art. For example, themachine 110 may be similar to machines 12, 14, 16 (FIG. 1) and mayembody an earth moving machine such as an excavator, a dozer, a loader,a backhoe, a motor grader, a dump truck, or any other earth movingmachine. The machine 110 may include one or more first sensors 120 a-120h (e.g., RADAR, LIDAR, infrared, thermal, audio, etc.) and one or moresecond sensors 140 a-140 d (e.g., image capture device, video camera,etc.). While the machine 110 is shown having eight first sensors 120a-120 h, and four second sensors 140 a-140 d, those skilled in the artwill appreciate that the machine 110 may include any number of sensorsarranged in any manner. The sensors 120, 140 may be similar to thesensors 20 a-c.

The first sensors 120 a-120 h and the second sensors 140 a-140 d may beincluded on the machine 110 during operation of the machine 110, e.g.,as the machine 110 moves about an area to complete certain tasks such asdigging, loosening, carrying, drilling, or compacting differentmaterials. In certain aspects, one or more of the first sensors 120a-120 h and the second sensors 140 a-140 d may be disposed onsite (e.g.,sensor 19 a (FIG. 1)) or offsite (e.g., sensor 19 b (FIG. 1)) duringoperation of the machine 110.

The machine 110 may use the first sensors 120 a-120 h to detect featuresof objects in their respective fields of view 130 a-130 h. For example,one of the first sensors 120 a may be configured to scan an area withina field of view 130 a to detect features of one or more objects. Duringoperation, one or more systems of the machine 110 may process sensordata received from one of the first sensor devices 120 a to detectfeatures that are in the environment of the machine 110. For example, amachine location system may use radar data to determine a position ofthe machine 110 relative to other machines or objects. Moreover, one ormore systems of the machine 110 may generate an alert, such as a sound,when the machine 110 is determined to be outside of a modeled positionor path. The second sensors 140 a-140 d may be attached to the frame ofthe machine 110 at a high vantage point. For example, the second sensors140 a-140 d may be attached to the top of the frame of the roof of themachine 110. The machine 110 may use the second sensors 140 a-140 d todetect features of objects in their respective fields of view. Forexample, the second sensors 140 a-140 d may be configured to recordimage data such as video or still images. As a further example, theimage data may include images of the material (e.g., payload) beingdisplaced by the machine 110. As such, the image data of the materialmay be processed to determine features of the material.

FIG. 4 is a schematic illustration of a worksite management system 26configured to receive and analyze data (e.g., communicated to thecentral station 18) relating to one or more machines (e.g., machines 12,14, 16 (FIG. 1) and/or machine 110 (FIG. 3)) or other sources (e.g.,operators). The worksite management system 26 may include an offboardcontroller 28 in remote communication with one or more sensors via thecentral station 18 and configured to process data from a variety ofsources and execute management methods at a worksite. For the purposesof this disclosure, the controller 28 may be primarily focused atpositively affecting performance irregularities and/or warningconditions experienced by the operators and/or the machines operating ata worksite such as the worksite 10 (FIG. 10). Positively affecting mayinclude reducing a likelihood of occurrence, reducing a magnitude of theirregularity, reducing a frequency of the irregularity, reducing aseverity of the irregularity, minimizing inefficiencies, identifyingcontributors to error, or otherwise improving machine and/or worksiteoperation associated with the irregularity.

The controller 28 may include any type of computer or a plurality ofcomputers networked together. The controller 28 may be located proximatethe mining operation of the worksite 10 or may be located at aconsiderable distance remote from the mining operation, such as in adifferent city or even a different country. It is also contemplated thatcomputers at different locations may be networked together to form thecontroller 28, if desired. In one aspect, the controller 28 may belocated onboard one or more of the machines, if desired.

The controller 28 may include among other things, a console 30, an inputdevice 32, an input/output device 34, a storage media 36, and acommunication interface 38. The console 30 may be any appropriate typeof computer display device that provides a graphical user interface(GUI) to display results and information to operators and other users ofthe worksite management system 26. The input device 32 may be providedfor operators to input information into the controller 28. The inputdevice 32 may include, for example, a keyboard, a mouse, touch screen,joystick, voice recognition, or another computer input device. Theinput/output device 34 may be any type of device configured toread/write information from/to a portable recording medium. Theinput/output device 34 may include among other things, a floppy disk, aCD, a DVD, a flash memory read/write device or the like. Theinput/output device 34 may be provided to transfer data into and out ofthe controller 28 using a portable recording medium. The storage media36 could include any means to store data within the controller 28, suchas a hard disk. The storage media 36 may be used to store a databasecontaining among others, historical worksite, machine, and operatorrelated data. The communication interface 38 may provide connectionswith the central station 18, enabling the controller 28 to be remotelyaccessed through computer networks, and means for data from remotesources to be transferred into and out of the controller 28. Thecommunication interface 38 may contain network connections, data linkconnections, and/or antennas configured to receive wireless data.

Data may be transferred to the controller 28 electronically or manually.Electronic transfer of data may include the remote transfer of datausing the wireless capabilities or the data link of the communicationinterface 38 by a communication channel as defined herein. Data may alsobe electronically transferred into the controller 28 through a portablerecording medium using the input/output device 34. Manually transferringdata into the controller 28 may include communicating data to a controlsystem operator in some manner, who may then manually input the datainto the controller 28 by way of, for example, the input device 32. Thedata transferred into the controller 28 may include machineidentification data, performance data, diagnostic data, and other data.The other data may include for example, weather data (current, historic,and forecast), machine maintenance and repair data, site data such assurvey information or soil test information, and other data known in theart.

The controller 28 may generate an analysis of the data collected fromone or more of onsite and offsite sensors and present results of theanalysis to a user of the worksite management system 26 and/or to theoperators of particular machines thereof by way of the communicationsinterface 38, for example. The results may include a productivityanalysis, an economic analysis (e.g., efficiency, fuel economy,operational cost, etc.), a cycle time analysis, an environmentalanalysis (e.g., engine emissions, road conditions, site conditions,etc.), or other analysis specific to each machine, each category ofmachines (e.g., digging machines, loading machines, hauling machines,etc.), each co-located machine, each operator associated with themachines, and/or for the worksite as a whole. In one aspect, results ofthe analysis may be indexed according to time, for example, according toa particular shift, a particular 24-hr period, or another suitableparameter (e.g., time period, liters of fuel, cost, etc.).

The results of the analysis may be in the form of detailed reports orthey may be summarized as a visual representation such as, for example,with an interactive graph. The results may be used to show a historicalperformance, a current performance, and/or an anticipated performance ofthe machines operating at the worksite. Alternatively or additionally,the results could be used to predict a progression of operations at theworksite and to estimate a time before the productivity, efficiency, orother performance measure of a particular machine, operator, group ofmachines, or the worksite becomes irregular (i.e., exceeds or fallsbelow a desired or expected limit). As an example, the results of theanalysis may indicate when a performance irregularity has occurred, iscurrently occurring, or anticipated to occur in the future. Thecontroller 28 may flag the user of the worksite management system 26 atthe time of the irregularity occurrence or during the analysis stagewhen the irregularity is first detected and/or anticipated.

For the purposes of this disclosure, a performance irregularity may bedefined as a non-failure deviation from a modeled performance such ashistorical, expected, or desired machine or worksite performance (e.g.,productivity, efficiency, emission, traffic congestion, or similarrelated performance) that is monitored, calculated, or otherwisereceived by the worksite management system 26 or other system. In oneaspect, an amount of deviation required for the irregularityclassification may be set by a machine operator, a user of the worksitemanagement system 26, a business owner, or other responsible entity. Insome situations, the performance irregularity may be indicative of asite condition over which little control may be exercised, but that maystill be accommodated to improve operations at the worksite.

Based on the analysis, when a performance irregularity has beendetermined to have occurred, be currently occurring, or is anticipatedto occur, the controller 28 may be configured to remotely reconfigure anoperational relationship of particular machines and thereby positivelyaffect the performance irregularity. The operational relationship may beassociated with, for example, the shift points included within atransmission map, engine valve and/or ignition timings included withinan engine calibration map, fuel settings included within a torque limitmap, maximum or minimum speed limits included within a travel limit map,steering boundaries included within a steering map, pressure and/orpriority settings included within a tool actuation map, or other similarsettings, limits, and/or boundaries contained within other softwaremaps, algorithms, and/or equations stored electronically within memory(e.g., a memory of the control module 20 (FIG. 2)). In general,reconfiguring the operational relationships described above may affecthow a particular machine responds to different situations. For example,reconfiguring the shift points of a transmission map may control theengine speed and/or wheel torques at which a transmission of aparticular machine shifts to a lower or higher gear combination.Similarly, changing engine valve and/or ignition timings of an enginecalibration map may control under what conditions intake and/or exhaustvalves open or close, at what point within an engine cycle thecombustion gas is energized, and resulting engine cylinder pressures andemissions. These changes to the operational relationships of the haulingmachine 16 may be implemented to improve productivity, efficiency, andemissions, or otherwise positively affect the performance irregularity,and may be maintained within the software maps, algorithms, and/orequations until a subsequent reconfiguration is implemented. In otherwords, reconfiguration of a machine's operational relationship may besemi-permanent and affect subsequent machine performance for an extendedperiod of time. Examples of reconfiguration implementation will beprovided in the following section.

FIG. 5 is a block diagram illustrating an exemplary system 200 that maybe installed on the machine 110 or in communication with the machine 110to detect and recognize features of objects in the environment of themachine 110. The system 200 may include one or more modules that whencombined perform object detection and recognition. For example, asillustrated in FIG. 5, the system 200 may include a first sensorinterface 205, a second sensor interface 206, a machine interface 207,an image transformer 210, a detector 215, a computing device 220, astate database 230, and a response module 250. While FIG. 5 showscomponents of system 200 as separate blocks, those skilled in the artwill appreciate that the functionality described below with respect toone component may be performed by another component, or that thefunctionality of one component may be performed by two or morecomponents. For example, the functionality of detector 215 may beperformed by the computing device 220, or the functionality of imagetransformer 210 may be performed by two components.

In certain aspects, the modules of the system 200 described above mayinclude logic embodied as hardware, firmware, or a collection ofsoftware written in a known programming language. The modules of system200 may be stored in any type of computer-readable medium, such as amemory device (e.g., random access, flash memory, and the like), anoptical medium (e.g., a CD, DVD, BluRay®, and the like), firmware (e.g.,an EPROM), or any other storage medium. The modules may be configuredfor execution by one or more processors to cause the system 200 toperform particular operations. The modules of the system 200 may also beembodied as hardware modules and may include connected logic units, suchas gates and flip-flops, and/or may include programmable units, such asprogrammable gate arrays or processors, for example.

The system 200 may include one or more of the first sensors 120 and oneor more of the second sensors 140. The first sensors 120 may correspondto one or more of the first sensors 120 a-120 h and the second sensors140 may correspond to one or more of the second sensors 140 a-140 d, forexample. Moreover, while only one of the first sensors 120 and one ofthe second sensors 140 are shown in FIG. 5, those skilled in the artwill appreciate that any number of sensors may be included in the system200.

In some aspects, before the system 200 may process sensor data from thefirst sensor 120 and sensor data from the second sensor 140, the sensordata must be converted to a format that is consumable by the modules ofsystem 200. Accordingly, the first sensor 120 may be connected to afirst sensor interface 205, and the second sensor 140 may be connectedto a second sensor interface 206. The first sensor interface 205 and thesecond sensor interface 206 may receive analog signals from theirrespective devices and convert them to digital signals which may beprocessed by the other modules of the system 200. For example, the firstsensor interface 205 may create digital RADAR or audio data usinginformation it receives from the first sensor 120, and second sensorinterface 206 may create digital image data using information itreceives from the second sensor 140. Other data may be generated basedon the respective type of sensor. In certain aspects, the first sensorinterface 205 and the second sensor interface 206 may package thedigital data in a data package or data structure along with metadatarelated to the converted digital data. For example, the first sensorinterface 205 may create a data structure or data package that hasmetadata and a payload representing the radar data from the first sensor120. Non-exhaustive examples of metadata related to the sensor data mayinclude the orientation of the first sensor 120, the position of thefirst sensor 120, and/or a time stamp for when the sensor data wasrecorded. Similarly, the second sensor interface 206 may create a datastructure or data package that has metadata and a payload representingimage data from the second sensor 140. Non-exhaustive examples ofmetadata related to the image data may include the orientation of thesecond sensor 140, the position of the second sensor 140 with respect tomachine 110, the down-vector of the second sensor 140, a time stamp forwhen the image data was recorded, and a payload field representing theimage data from the second sensor 140.

In certain aspects, the first sensor 120 and the second sensor 140 maybe digital devices that produce data, and the first sensor interface 205and the second sensor interface 206 may package the digital data into adata structure for consumption by the other modules of the system 200.The first sensor interface 205 and the second sensor interface 206 mayexpose an application program interface (API) that exposes one or morefunction calls allowing the other modules of the system 200, such as thedetector 215, to access the sensor data.

In certain aspects, the system 200 may include a machine interface 207the may communicate with one or more sensors deployed on machine 110 andmay translate signals from the one or more sensors to digital data thatmay be consumed by the modules of the system 200. The digital data mayinclude operational state data that includes information related tomachine's 110 current operation. For example, the operational state datamay include the current speed of the machine 110, the current directionof the machine 110 (e.g., forward or backward), the current steeringangle of the machine 110, the acceleration of the machine 110, adeformation of a portion of the machine 110, a location of the machine110, a state of a component of the machine such as a valve, actuator,spring, or other state of the device. The operational state data mayalso include information about tools or other work components of themachine 110. For example, the operational state data may include theposition of loading or digging arms, or the angle/position of a load bedattached to the machine 110. The operational state data may also includemetadata such as a time stamp or an identifier of the tool or workcomponent to which the operational state data applies. The machineinterface 207 may expose an API providing access to the operationalstate data of the machine 110 to the modules of the system 200, such asthe response module 250 and the detector 215.

The detector 215 may be configured to accesses or receive data from thefirst sensor interface 205 and the second sensor interface 206 andprocesses it to detect features of objects that are in the environmentof the machine 110. The data accessed from the first sensor interface205 may include an indication that a material load (e.g., payload) wasdetected in the environment of the machine 110. The detector 215 mayaccess such data by periodically polling the first sensor interface 205for sensor data and analyzing the data to determine if the dataindicates the presence of the material load and/or any identifiablefeatures of the material load. The detector 215 may also access the datathrough an event or interrupt triggered by the first sensor interface205. For example, when the first sensor 120 detects an object, it maygenerate a signal that is received by the first sensor interface 205,and the first sensor interface 205 may publish an event to its APIindicating that the first sensor 120 has detected an object. Thedetector 215, having registered for the event through the API of thefirst sensor interface 205, may receive the data and analyze the data todetermine whether an object such as a material load has been detected.As an illustrative example, once the presence of a material load hasbeen detected via radar, for example, the detector 215 may access imagedata through the second sensor interface 206 and process the image datato determine the type of material and/or other material characteristicssuch as material to air ratio. As another illustrative example, thefirst sensors 120 may detect an audible sound and the detector 215 mayaccess image data through the second sensor interface 206 in response tothe detection of the sound. As a further illustrative example, the firstsensors 120 may be offsite and may detect an operational condition(e.g., worksite event) that needs further investigation. As such, thedetector 215 may access image data through the second sensor interface206 in response to the detection of the operational condition.

As processing image data is computationally expensive, the detector 215may advantageously limit the amount of image data that is processed byusing data accessed or received via the first sensor 120 or first sensorinterface 205. The data accessed or received via the first sensor 120 orfirst sensor interface 205 may be used, for example, to limit processingto the parts of the image data where a particular feature is expected.For example, the detector 215 may map accessed first sensor data toaccessed second sensor data and only process the portions of the secondsensor data that correspond to a particular feature of interest. Thedetector 215 may map various data sets using metadata related to theorientation and position of the first sensor 120 and the second sensor140. For example, when the detector 215 receives data from the firstsensor 120 positioned on the rear of machine 110, it may map that datato data from the second sensor 140 that is also positioned on the rearof machine 110.

The detector 215 may also process image data to detect features withinthe image data. The detector 215 may detect features in the image byusing edge detection techniques. For example, the detector 215 mayanalyze the image data for places where image brightness changes sharplyor has discontinuities. The detector 215 may employ a known edgedetection technique such as a Canny edge detector. Although edgedetection is one method by which the detector 215 may detect features inimages, those skilled in the relevant art will appreciate that othermethods for detecting objects in image data may be used to achieve thesame effect.

When the detector 215 detects a feature in the received data, it mayprovide detected feature data to the computing device 220 to classifythe detected feature according to a classification model. The detectedfeature data provided by the detector 215 may include metadata relatedto the detected feature. Non-exhaustive examples of metadata for thedetected feature data may include the position of the feature within theimage data, the distance of the detected feature from the first sensor120, and/or the angular position of the detected feature. The detectedfeature data may include the output of edge detection, that is, imagedata that describes the shape of the feature, for example. Once thecomputing device 220 receives the detected feature data it may comparethe feature to learned or known signatures.

As the computing device 220 may rely on the shape of detected features,the format and orientation of the images recorded by the second sensor140 may affect accuracy. For example, the second sensor 140 may be awide-angle top-down view camera, birds-eye view camera, fisheye camera,or some other camera that produces an image that is from a perspectiveother than a ground level perspective. As a result, the images producedby the second sensor 140 may include objects oriented on their sides asopposed to upright. Accordingly, in certain aspects, the system 200 mayinclude an image transformer 210 that transforms image data received bythe second sensor interface 206 so that the computing device 220 doesnot need to account for object orientation when classifying an object.

In an aspect, the computing device 220 may use one or moreclassification models to determine the type of feature detected by thedetector 215. For example, as illustrated in FIG. 5, the computingdevice 220 may use a machine model 221, a payload model 222, or aworksite model 223 to classify a detected features as a piece ofequipment, a machine in a particular state, a type of payload material,a characteristic of material, and/or as a normal operation orirregular/inefficient operation. As an example, the computing device 220may compare the detected feature data to the classification models anddetermine whether the detected feature data is consistent withparameters of the classification model.

In an aspect, a state database 230 may include information relating toone or more known and/or catalogued machine state signatures (e.g.,models). Such signatures may represent various data types such as image,audio, RADAR, LIDAR, thermal, infrared, etc. Such signatures may becatalogued to represent specific identifiable machine states. Forexample, a specific audio sound may be stored as an audio signature fora particular machine operation such as a loading or dumping operation.As another example, a specific image may be stored as an image signaturerepresenting a full load for a particular machine. As another example, aspecific image may represent best practices, safety protocols, or aregular/normal operation for a particular machine. Such signatures maybe manually catalogued or may be processed on large data sets (e.g., viamachine learning) to generate a predictor that represents theidentifiable signature.

The signatures may be compared to subsequent data (e.g., detectedfeature data) to determine if the subsequent data matches any of thesignatures. As such, the computing device 220 may classify newlyreceived data as a particular machine state based on the comparison withthe signatures (e.g., model). As a further example, the computing device220 may process site data 224 for comparison with the detected featuredata to determine a matching signature of a machine state.Classification of data relating to machine operations may includeclassifying certain operations as normal/regular and others asirregular. Using the stored signatures of learned patterns, theclassification process may be used to detect when site operations arenormal or within safety parameters and when certain operations areirregular or outside safety protocols. In an aspect, image signaturesmay help to identify machine state in situations where machine is beingused in a non-typical manner, such as a dozer being used to load atruck. Conventional sensor techniques may not be configured todisambiguate the overall process without the inclusion of the imagesignature.

In an aspect, the site data 224 may include performance information suchas information relating to the theoretical or projected performancecharacteristics of the machines operating at the worksite. As examples,performance information may include a projected loading rate of theloading machine (e.g., tons loaded per hour), a projected processingrate of a processing machine (e.g., tons processed per hour), aprojected carrying capacity of the hauling machine (e.g., tons ofmaterial per load), a projected maximum safe travel speed of the haulingmachine or the like. Performance information may also include projectedperformance metrics relating to the cooperative operation of more thanone machine. For example, performance information may include theprojected amount of time that the loading machine should take to fillthe bed of a particularly-sized hauling machine. As another example,performance information may include the projected cycle time of acomplete cycle of the loading machine filling the hauling machine, thehauling machine delivering its payload to a processing machine, and thehauling machine returning again to the loading machine.

The site data 224 may include information pertaining to the roads of theworksite. For example, this may include information on the materialcomposition of a road (e.g., paved, dirt, mud or the like). Roadinformation may also include the weight-bearing capacity of a road(e.g., 100 tons), the maximum speed at which machines may safely operateon a road, or a metric indicating the level of deterioration of a road.The site data 224 may include a designation of a hauling route over oneor more roads.

The site data 224 may include cost-related information. Cost-relatedinformation may include a purchase cost of a machine, a lease cost of amachine, an operating cost of a machine (e.g., fuel, wear-and-teardeterioration) or the like. Other cost-related information may includewage costs for personnel associated with the worksite, including thosepersonnel operating the machines. Cost-related information mayadditionally include road construction cost, road maintenance cost, andpower costs such as for electricity or natural gas. As a furtherexample, the site data 224 may include information pertaining to sitegoals. For example, site goal information may include a goal cost ofoperation or a goal productivity level (e.g., a particular amount ofmaterial processing in a specified period of time).

The site data 224 may include a worksite model 223 representing thehistorical, current, and/or predictive operations of a worksite,including one or more operations of a machine (e.g., machines 12, 14, 16(FIG. 1)). For example, and referring back to the exemplary worksite 10depicted in FIG. 1, the worksite model 223 may simulate the operation ofthe loading machine 14 depositing a material into the hauling machine16. The worksite model 223 may, in turn, simulate the laden haulingmachine 16 traveling along a road and unloading its payload to aprocessing machine, wherein the delivered material is simulated beingprocessed. The worksite model 223 may then simulate the empty haulingmachine 16 traveling back over the road to repeat the process. The sitesimulation may be determined by the controller 28 or other processor.For example, the site simulation may be determined at a server or otherprocessor controlled by a third-party and subsequently delivered to andreceived by the controller 28.

In certain aspects, site analytics are performed. Site analytics may beperformed, for example via the computing device 220, to identify acurrent inefficiency, predict an future inefficiency, or identify arecreated historical inefficiency at the worksite, for example, based onone or more of the models and/or the site data 224. An inefficiency mayrelate to any aspect of the operations of the worksite and may includean exceeding of a pre-specified threshold relating to that aspect. Asused herein, exceeding of a pre-specified threshold may refer to eithera numerical aspect being greater than a pre-specified threshold or lessthan a pre-specified threshold, according to the context of usage.Examples of an inefficiency may include an indication that the haulingmachines are sitting idle for a time period exceeding a threshold (e.g.,longer than one minute) while waiting for the loading machine to finishloading its current load or an indication that the hauling machines on aroad are traveling below a threshold relating to their maximum safespeed (e.g., less than twenty miles per hour) due to traffic congestionon the road. Other examples may include an indication that the worksiteis processing less material than projected, an indication that a machineis consuming more fuel than projected, or an indication that the haulingmachine is having a longer loading/unloading cycle time than projected.Note that the aforementioned examples apply equally in the predictivesense, e.g., predicting that the hauling machines will sit idle, and inthe historical sense, e.g., identifying that the hauling machines weresitting idle.

Site analytics may additionally include identifying a factor which maycontribute to or may have contributed to an identified currentinefficiency, an identified future inefficiency, or an identifiedhistorical inefficiency. For example, if an identified inefficiencyincludes an indication that the hauling machine is sitting idle whilewaiting for the loading machine for a period beyond a pre-specifiedthreshold, the site analytics may identify a factor contributing to theidleness, such as the loading machine not capturing a full shovel ofmaterial. In an aspect, such mixed fleet analytics incorporatingin-network and out-of-network machines facilities macro-level analysisat the fleet level, or job-level, or multi-machine level, whilemachine-centric data such as localized video analytics may provide amicro-level analysis. As an example, certain machines work in tandem,such as a tractor pushing a scraper on an earthworks jobsite. Mixedfleet analytics may be used for a macro-level analysis of the operationincluding the tandem machines working together to move earth.Additionally, micro-level analytics may be used to analyze eachindividual machine to measure how much earth is being filled in abucket, for example.

In an aspect, based upon the classification of the computing device 220,the response module 250 may generate a response. The response may begenerated and further transmitted by a central location (e.g., thecentral station 18 (FIG. 1)) or by a device on a machine, for example. Aresponse may include an instruction relating to an inefficiencyidentified by the computing device 220. The response may include anelectronic communication (e.g., email, short message service (SMS) textmessage, or text file). The generated response may include a warning,for example, to an operator, foreman, site supervisor, or administrator.The warning may include a visual indicator such as a message or icon tobe displayed on a display system and directed towards an operator orother personnel at the worksite. The instruction or warning may includeon variable level of severity.

In an aspect, based upon the classification of the computing device 220,a module 260 may be configured to update one or more of the models 221,222, 223. This may include transmitting update classification,signatures, learned correlations, and the like, which was not includedin the previous model. This may additionally include transmitting aninefficiency or factor contributing to an inefficiency identified in thesite analytics of the computing device 220 so that the inefficiency orfactor may be incorporated into an updated model.

INDUSTRIAL APPLICABILITY

The industrial applicability of the System, methods, and computerreadable medium for determining machine states at an mixed fleetworksite (e.g., construction site, minesite, etc.) using at least videoand/or audio analytics herein described will be readily appreciated fromthe foregoing discussion. Although various machines 12, 14, 16 aredescribed in relation to FIG. 1, those skilled in the art may understandthat the machine 12, 14, 16 is not so limited and may include any mannerof work vehicle or equipment that may be found at a worksite. Similarly,although the hauling machine 16 is depicted in FIG. 2, any type of workvehicle or equipment may be used.

FIG. 6 illustrates a process flow chart for a method 300 for worksiteoperation optimization based on determining machine state of a mixedfleet at a worksite. For illustration, the operations of the method 300will be discussed in reference to FIGS. 1-5. At step 302, site data 224may be received. As an example, site data 224 may be received by thecomputing device 220 (e.g., central station 18). The site data 224 maybe previously stored on the computing device 220 or may be received fromanother server or processor, including one associated with a thirdparty. The site data 224 may include theoretical or projectedperformance characteristics of machines 12, 14, 16 operating at theworksite 10 (e.g., a projected loading rate of the loading machine 14)or performance characteristics of machines 12 operating in conjunction(e.g., a cycle time of the loading machine 14 loading the haulingmachine 16, the hauling machine 16 traveling to a processing machine,and the processing machine receiving the hauling machine's 110 payload).The site data 224 may include data on a road of the worksite 10, such asthe expected material composition, layout, weight-bearing capacity,maximum speed, or a metric indicating a level of deterioration of theroad. The site data 224 may additionally include cost-related data, suchas the purchase or lease cost of a machine 12, 14, 16, wage costs foroperators or other personnel, road costs, or power costs. The site data224 may include site goal data, such as a goal for cost of operation orproductivity level for the worksite 10.

At step 304, a model such as the machine model 221, payload model 222,and/or worksite model 223 may be received. The model may be received oraccessed by the computing device 220, for example, after the model isdetermined based, at least in part, on the site data 224 or storedmodels (e.g., signatures) via the state database 230. The determinationof the model may be performed by the computing device 220 or anotherprocessor, including one controlled by a different party than thatcontrolling the computing device 220.

At step 306, first sensor data may be received by, for example, from oneor more of the first sensors 120. As an example, the first sensor datais received by the computing device 220, directly or indirectly, and maybe pre-processed by other components such as the detector 215. The firstsensor data may comprise sensor data such as audio, video, infrared,RADAR, LIDAR, thermal, and the like. The first sensor data may relate toan in-network machine of the machine 12, 14, 16 (FIG. 1).

At step 308, second sensor data may be received by, for example, fromone or more of the second sensors 120. As an example, the second sensordata is received by the computing device 220, directly or indirectly,and may be pre-processed by other components such as the detector 215.The second sensor data may include sensor data such as audio, video,infrared, RADAR, LIDAR, thermal, and the like. In certain aspects, thesecond sensor data is received from a different type of sensor from thefirst sensor data. As an example, the first sensor data may be audiodata and the second sensor data may be image data. As another example,the first sensor data may be received from an offsite sensor, while thesecond sensor data is received from an onsite sensor. In other aspect,the second sensor data may relate to an out-of-network machine of themachines 12, 14, 16 (FIG. 1).

At step 310, site analytics are performed by, for example, the computingdevice 220. The site analytics may be performed based on received sitedata 224 and/or models. Site analytics may include identifying andclassify normal operations and irregular operations. Site analytics maybe used to detect a current inefficiency, predicting a futureinefficiency, or identifying a historical inefficiency at the worksite10. An inefficiency may relate to an aspect of the operations of theworksite 10 and may be determined by data relating to that aspectexceeding a pre-specified threshold. Examples of an inefficiency mayinclude an indication that the hauling machines 16 are sitting idle fora time period exceeding a threshold (e.g., longer than one minute) whilewaiting for the loading machine 14 to finish loading its current load oran indication that the hauling machines 16 on a road are traveling belowa threshold relating to their maximum safe speed (e.g., less than twentymiles per hour) due to traffic congestion on the road. Site analyticsmay additionally include identifying a factor which may contribute to ormay have contributed to an identified current inefficiency, anidentified future inefficiency, or an identified historicalinefficiency.

At step 312, a response is generated by, for example, the responsemodule 250 based on the site analytics. A response may include aninstruction relating to an operator warning condition such as aninefficiency or factor contributing to an inefficiency and may beembodied in an electronic communication such as an email, SMS textmessage, or text file. The response may be configured to result in aremediation of the operator warning condition. A response may alsoinclude a warning directed to, for example, an operator. A warning mayinclude an electronic communication such as an email, SMS text, or textfile or may include an electronic instruction for a warning light to belit or an audio alarm to be sounded, for example.

The generated response may further be transmitted to, for example, adisplay system (e.g., operator interface module 22) of a machine 12, 14,16, so that the operator may be apprised of an instruction or warning.The response may additionally be transmitted to another electronicdevice, such as a smartphone, tablet computer, laptop, or personalcomputer of the operator or other worksite 10 personnel (e.g., foremanor supervisor). The response may be transmitted by, for example, thecentral station 18. The response may be used to update one or more ofthe site data 224 and/or the models 221, 222, 223. As an example, thesecond sensor data may relate to an out-of-network machine and such datamay be used to supplement a model relating to a mixed fleet worksite.

Whether such functionality is implemented as hardware or softwaredepends upon the design constraints imposed on the overall system.Skilled persons may implement the described functionality in varyingways for each particular application, but such implementation decisionsshould not be interpreted as causing a departure from the scope of thedisclosure. In addition, the grouping of functions within a module,block, or step is for ease of description. Specific functions or stepsmay be moved from one module or block without departing from thedisclosure.

The various illustrative logical blocks and modules described inconnection with the aspects disclosed herein may be implemented orperformed with a general purpose processor, a digital signal processor(DSP), application specific integrated circuit (ASIC), a fieldprogrammable gate array (FPGA) or other programmable logic device,discrete gate or transistor logic, discrete hardware components, or anycombination thereof designed to perform the functions described herein.A general-purpose processor may be a microprocessor, but in thealternative, the processor may be any processor, controller,microcontroller, or state machine. A processor may also be implementedas a combination of computing devices, for example, a combination of aDSP and a microprocessor, a plurality of microprocessors, one or moremicroprocessors in conjunction with a DSP core, or any other suchconfiguration.

The steps of a method or algorithm described in connection with theaspects disclosed herein may be embodied directly in hardware, in asoftware module executed by a processor (e.g., of a computer), or in acombination of the two. A software module may reside, for example, inRAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory,registers, hard disk, a removable disk, a CD-ROM, or any other form ofstorage medium. An exemplary storage medium may be coupled to theprocessor such that the processor may read information from, and writeinformation to, the storage medium. In the alternative, the storagemedium may be integral to the processor. The processor and the storagemedium may reside in an ASIC.

In at least some aspects, a processing system (e.g., control module 20,controller 28, etc.) that implements a portion or all of one or more ofthe technologies described herein may include a general-purpose computersystem that includes or is configured to access one or morecomputer-accessible media.

FIG. 7 depicts a general-purpose computer system that includes or isconfigured to access one or more computer-accessible media. In theillustrated aspect, a computing device 400 may include one or moreprocessors 410 a, 410 b, and/or 410 n (which may be referred hereinsingularly as the processor 410 or in the plural as the processors 410)coupled to a system memory 420 via an input/output (I/O) interface 430.The computing device 400 may further include a network interface 440coupled to an I/O interface 430.

In various aspects, the computing device 400 may be a uniprocessorsystem including one processor 410 or a multiprocessor system includingseveral processors 410 (e.g., two, four, eight, or another suitablenumber). The processors 410 may be any suitable processors capable ofexecuting instructions. For example, in various aspects, theprocessor(s) 410 may be general-purpose or embedded processorsimplementing any of a variety of instruction set architectures (ISAs),such as the x86, PowerPC, SPARC, or MIPS ISAs, or any other suitableISA. In multiprocessor systems, each of the processors 410 may commonly,but not necessarily, implement the same ISA.

In some aspects, a graphics processing unit (“GPU”) 412 may participatein providing graphics rendering and/or physics processing capabilities.A GPU may, for example, include a highly parallelized processorarchitecture specialized for graphical computations. In some aspects,the processors 410 and the GPU 412 may be implemented as one or more ofthe same type of device.

The system memory 420 may be configured to store instructions and dataaccessible by the processor(s) 410. In various aspects, the systemmemory 420 may be implemented using any suitable memory technology, suchas static random access memory (“SRAM”), synchronous dynamic RAM(“SDRAM”), nonvolatile/Flash®-type memory, or any other type of memory.In the illustrated aspect, program instructions and data implementingone or more desired functions, such as those methods, techniques anddata described above, are shown stored within the system memory 420 ascode 425 and data 426.

In one aspect, the I/O interface 430 may be configured to coordinate I/Otraffic between the processor(s) 410, the system memory 420 and anyperipherals in the device, including a network interface 440 or otherperipheral interfaces. In some aspects, the I/O interface 430 mayperform any necessary protocol, timing or other data transformations toconvert data signals from one component (e.g., the system memory 420)into a format suitable for use by another component (e.g., the processor410). In some aspects, the I/O interface 430 may include support fordevices attached through various types of peripheral buses, such as avariant of the Peripheral Component Interconnect (PCI) bus standard orthe Universal Serial Bus (USB) standard, for example. In some aspects,the function of the I/O interface 430 may be split into two or moreseparate components, such as a north bridge and a south bridge, forexample. Also, in some aspects some or all of the functionality of theI/O interface 430, such as an interface to the system memory 420, may beincorporated directly into the processor 410.

The network interface 440 may be configured to allow data to beexchanged between the computing device 400 and other device or devices460 attached to a network or networks 450, such as other computersystems or devices, for example. In various aspects, the networkinterface 440 may support communication via any suitable wired orwireless general data networks, such as types of Ethernet networks, forexample. Additionally, the network interface 440 may supportcommunication via telecommunications/telephony networks, such as analogvoice networks or digital fiber communications networks, via storagearea networks, such as Fibre Channel SANs (storage area networks), orvia any other suitable type of network and/or protocol.

In some aspects, the system memory 420 may be one aspect of acomputer-accessible medium configured to store program instructions anddata as described above for implementing aspects of the correspondingmethods and apparatus. However, in other aspects, program instructionsand/or data may be received, sent, or stored upon different types ofcomputer-accessible media. Generally speaking, a computer-accessiblemedium may include non-transitory storage media or memory media, such asmagnetic or optical media, e.g., disk or DVD/CD coupled to computingdevice the 400 via the I/O interface 430. A non-transitorycomputer-accessible storage medium may also include any volatile ornon-volatile media, such as RAM (e.g., SDRAM, DDR SDRAM, RDRAM, SRAM,etc.), ROM, etc., that may be included in some aspects of the computingdevice 400 as the system memory 420 or another type of memory. Further,a computer-accessible medium may include transmission media or signals,such as electrical, electromagnetic or digital signals, conveyed via acommunication medium, such as a network and/or a wireless link, such asthose that may be implemented via the network interface 440. Portions orall of multiple computing devices, such as those illustrated in FIG. 7,may be used to implement the described functionality in various aspects;for example, software components running on a variety of differentdevices and servers may collaborate to provide the functionality. Insome aspects, portions of the described functionality may be implementedusing storage devices, network devices or special-purpose computersystems, in addition to or instead of being implemented usinggeneral-purpose computer systems. The term “computing device,” as usedherein, refers to at least all these types of devices and is not limitedto these types of devices.

It should also be appreciated that the systems in the figures are merelyillustrative and that other implementations might be used. Additionally,it should be appreciated that the functionality disclosed herein mightbe implemented in software, hardware, or a combination of software andhardware. Other implementations should be apparent to those skilled inthe art. It should also be appreciated that a server, gateway, or othercomputing node may include any combination of hardware or software thatmay interact and perform the described types of functionality, includingwithout limitation desktop or other computers, database servers, networkstorage devices and other network devices, PDAs, tablets, cellphones,wireless phones, pagers, electronic organizers, Internet appliances, andvarious other consumer products that include appropriate communicationcapabilities. In addition, the functionality provided by the illustratedmodules may in some aspects be combined in fewer modules or distributedin additional modules. Similarly, in some aspects the functionality ofsome of the illustrated modules may not be provided and/or otheradditional functionality may be available.

Each of the operations, processes, methods, and algorithms described inthe preceding sections may be embodied in, and fully or partiallyautomated by, code modules executed by at least one computer or computerprocessors. The code modules may be stored on any type of non-transitorycomputer-readable medium or computer storage device, such as harddrives, solid state memory, optical disc, and/or the like. The processesand algorithms may be implemented partially or wholly inapplication-specific circuitry. The results of the disclosed processesand process steps may be stored, persistently or otherwise, in any typeof non-transitory computer storage such as, e.g., volatile ornon-volatile storage.

The various features and processes described above may be usedindependently of one another, or may be combined in various ways. Allpossible combinations and sub-combinations are intended to fall withinthe scope of this disclosure. In addition, certain method or processblocks may be omitted in some implementations. The methods and processesdescribed herein are also not limited to any particular sequence, andthe blocks or states relating thereto may be performed in othersequences that are appropriate. For example, described blocks or statesmay be performed in an order other than that specifically disclosed, ormultiple blocks or states may be combined in a single block or state.The exemplary blocks or states may be performed in serial, in parallel,or in some other manner. Blocks or states may be added to or removedfrom the disclosed example aspects. The exemplary systems and componentsdescribed herein may be configured differently than described. Forexample, elements may be added to, removed from, or rearranged comparedto the disclosed example aspects.

It will also be appreciated that various items are illustrated as beingstored in memory or on storage while being used, and that these items orportions of thereof may be transferred between memory and other storagedevices for purposes of memory management and data integrity.Alternatively, in other aspects some or all of the software modulesand/or systems may execute in memory on another device and communicatewith the illustrated computing systems via inter-computer communication.Furthermore, in some aspects, some or all of the systems and/or modulesmay be implemented or provided in other ways, such as at least partiallyin firmware and/or hardware, including, but not limited to, at least oneapplication-specific integrated circuits (ASICs), standard integratedcircuits, controllers (e.g., by executing appropriate instructions, andincluding microcontrollers and/or embedded controllers),field-programmable gate arrays (FPGAs), complex programmable logicdevices (CPLDs), etc. Some or all of the modules, systems and datastructures may also be stored (e.g., as software instructions orstructured data) on a computer-readable medium, such as a hard disk, amemory, a network, or a portable media article to be read by anappropriate drive or via an appropriate connection. The systems,modules, and data structures may also be transmitted as generated datasignals (e.g., as part of a carrier wave or other analog or digitalpropagated signal) on a variety of computer-readable transmission media,including wireless-based and wired/cable-based media, and may take avariety of forms (e.g., as part of a single or multiplexed analogsignal, or as multiple discrete digital packets or frames). Suchcomputer program products may also take other forms in other aspects.Accordingly, the disclosure may be practiced with other computer systemconfigurations.

Conditional language used herein, such as, among others, “may,” “could,”“might,” “may,” “e.g.,” and the like, unless specifically statedotherwise, or otherwise understood within the context as used, isgenerally intended to convey that certain aspects include, while otheraspects do not include, certain features, elements, and/or steps. Thus,such conditional language is not generally intended to imply thatfeatures, elements, and/or steps are in any way required for at leastone aspects or that at least one aspects necessarily include logic fordeciding, with or without author input or prompting, whether thesefeatures, elements, and/or steps are included or are to be performed inany particular aspect. The terms “comprising,” “including,” “having,”and the like are synonymous and are used inclusively, in an open-endedfashion, and do not exclude additional elements, features, acts,operations, and so forth. Also, the term “or” is used in its inclusivesense (and not in its exclusive sense) so that when used, for example,to connect a list of elements, the term “or” means one, some, or all ofthe elements in the list.

While certain example aspects have been described, these aspects havebeen presented by way of example only, and are not intended to limit thescope of aspects disclosed herein. Thus, nothing in the foregoingdescription is intended to imply that any particular feature,characteristic, step, module, or block is necessary or indispensable.Indeed, the novel methods and systems described herein may be embodiedin a variety of other forms; furthermore, various omissions,substitutions, and changes in the form of the methods and systemsdescribed herein may be made without departing from the spirit ofaspects disclosed herein. The accompanying claims and their equivalentsare intended to cover such forms or modifications as would fall withinthe scope and spirit of certain aspects disclosed herein.

The preceding detailed description is merely exemplary in nature and isnot intended to limit the disclosure or the application and uses of thedisclosure. The described aspects are not limited to use in conjunctionwith a particular type of machine. Hence, although the presentdisclosure, for convenience of explanation, depicts and describesparticular machine, it will be appreciated that the assembly andelectronic system in accordance with this disclosure may be implementedin various other configurations and may be used in other types ofmachines. Furthermore, there is no intention to be bound by any theorypresented in the preceding background or detailed description. It isalso understood that the illustrations may include exaggerateddimensions to better illustrate the referenced items shown, and are notconsider limiting unless expressly stated as such.

It will be appreciated that the foregoing description provides examplesof the disclosed system and technique. However, it is contemplated thatother implementations of the disclosure may differ in detail from theforegoing examples. All references to the disclosure or examples thereofare intended to reference the particular example being discussed at thatpoint and are not intended to imply any limitation as to the scope ofthe disclosure more generally. All language of distinction anddisparagement with respect to certain features is intended to indicate alack of preference for those features, but not to exclude such from thescope of the disclosure entirely unless otherwise indicated.

The disclosure may include communication channels that may be any typeof wired or wireless electronic communications network, such as, e.g., awired/wireless local area network (LAN), a wired/wireless personal areanetwork (PAN), a wired/wireless home area network (HAN), awired/wireless wide area network (WAN), a campus network, a metropolitannetwork, an enterprise private network, a virtual private network (VPN),an internetwork, a backbone network (BBN), a global area network (GAN),the Internet, an intranet, an extranet, an overlay network, a cellulartelephone network, a Personal Communications Service (PCS), using knownprotocols such as the Global System for Mobile Communications (GSM),CDMA (Code-Division Multiple Access), Long Term Evolution (LTE), W-CDMA(Wideband Code-Division Multiple Access), Wireless Fidelity (Wi-Fi),Bluetooth, and/or the like, and/or a combination of two or more thereof.

Additionally, the various aspects of the disclosure may be implementedin a non-generic computer implementation. Moreover, the various aspectsof the disclosure set forth herein improve the functioning of the systemas is apparent from the disclosure hereof. Furthermore, the variousaspects of the disclosure involve computer hardware that it specificallyprogrammed to solve the complex problem addressed by the disclosure.Accordingly, the various aspects of the disclosure improve thefunctioning of the system overall in its specific implementation toperform the process set forth by the disclosure and as defined by theclaims.

Recitation of ranges of values herein are merely intended to serve as ashorthand method of referring individually to each separate valuefalling within the range, unless otherwise indicated herein, and eachseparate value is incorporated into the specification as if it wereindividually recited herein. All methods described herein may beperformed in any suitable order unless otherwise indicated herein orotherwise clearly contradicted by context.

We claim:
 1. A method comprising: receiving, by one or more processors,one or more models relating to a worksite or a fleet of machines at theworksite, wherein the fleet of machines comprises an in-network machineand an out-of-network machine; receiving, by the one or more processors,first sensor data associated with the out-of-network machine at theworksite, the first sensor data comprising one or more of image data andaudio data; receiving, by the one or more processors, second sensor dataassociated with the in-network machine at the worksite; determining, bythe one or more processors, a machine state of each of the in-networkmachine and the out-of-network machine based at least on the firstsensor data and the second sensor data; comparing the determined machinestates to a modeled machine state represented by the received one ormore models to detect an irregularity in site operations or aninefficiency in site operations; and generating a response based atleast on the detected irregularity or inefficiency.
 2. The method ofclaim 1, further comprising transmitting the response to one or more ofan operator and a central station.
 3. The method of claim 1, wherein theone or more models comprises a worksite model and further comprisingupdating the worksite model based at least on the generated response. 4.The method of claim 1, wherein the first sensor data is received via anonsite sensor and the second sensor data is received via an offsitesensor.
 5. The method of claim 1, wherein the first sensor data isreceived via an offsite sensor and the second sensor data is receivedvia an onsite sensor.
 6. The method of claim 1, wherein one or more ofthe determined machine state and the modeled machine state comprise acharacteristic of a material load.
 7. The method of claim 1, wherein thedetermining the machine state comprises generating a signaturerepresenting the machine state.
 8. The method of claim 1, wherein theresponse comprises one or more of an audible indicator and a visualindicator.
 9. A system comprising: a processor; and a memory bearinginstructions that, upon execution by the processor, cause the system atleast to: receive one or more models relating to a worksite or a fleetof machines at the worksite, wherein the fleet of machines comprises anin-network machine and an out-of-network machine; receiving, by the oneor more processors, first sensor data associated with the out-of-networkmachine at the worksite, the first sensor data comprising one or more ofimage data and audio data; receive second sensor data associated withthe in-network machine at the worksite; determine a machine state ofeach of the in-network machine and the out-of-network machine based atleast on the first sensor data and the second sensor data; and comparethe determined machine states to a modeled machine state represented bythe received one or more models to classify one or more site operations.10. The system of claim 9, wherein the one or more models comprises aworksite model and further comprising instructions that, upon executionby the processor, cause the system at least to update the worksite modelbased at least on the classified one or more site operations.
 11. Thesystem of claim 9, wherein the first sensor data is received via anonsite sensor and the second sensor data is received via an offsitesensor.
 12. The system of claim 9, wherein the first sensor data isreceived via an offsite sensor and the second sensor data is receivedvia an onsite sensor.
 13. The system of claim 9, wherein one or more ofthe determined machine state and the modeled machine state comprise acharacteristic of a material load.
 14. The system of claim 9, whereinthe determining the machine state comprises generating a signaturerepresenting the machine state.
 15. A computer readable storage mediumbearing instructions that, upon execution by one or more processors,effectuate operations comprising: receiving, by the one or moreprocessors, one or more models relating to a worksite or a fleet ofmachines at the worksite, wherein the fleet of machines comprises anin-network machine and an out-of-network machine; receiving, by the oneor more processors, first sensor data associated with the out-of-networkmachine at the worksite, the first sensor data comprising one or more ofimage data and audio data; receiving, by the one or more processors,second sensor data associated with the in-network machine at theworksite; determining, by the one or more processors, a machine state ofeach of the in-network machine and the out-of-network machine based atleast on the first sensor data and the second sensor data; comparing thedetermined machine states to a modeled machine state represented by thereceived one or more models to detect an irregularity in site operationsor an inefficiency in site operations; and generating a response basedat least on the detected irregularity or inefficiency.
 16. The computerreadable storage medium of claim 15, wherein the first sensor data isreceived via an onsite sensor and the second sensor data is received viaan offsite sensor.
 17. The computer readable storage medium of claim 15,wherein the first sensor data is received via an offsite sensor and thesecond sensor data is received via an onsite sensor.
 18. The computerreadable storage medium of claim 15, wherein one or more of thedetermined machine state and the modeled machine state comprise acharacteristic of a material load.
 19. The computer readable storagemedium of claim 15, wherein the detecting an irregularity in siteoperations or an inefficiency in site operations comprises comparing thedetermined machine state to one or more models relating to the worksite.20. The computer readable storage medium of claim 15, further comprisingtransmitting the response to one or more of an operator and a centralstation.